Integrating longitudinal information in hippocampal volume measurements for the early detection of Alzheimer's disease.

Abstract Background Structural MRI measures for monitoring Alzheimer's Disease (AD) progression are becoming instrumental in the clinical practice, and more so in the context of longitudinal studies. This investigation addresses the impact of four image analysis approaches on the longitudinal performance of the hippocampal volume. Methods We present a hippocampal segmentation algorithm and validate it on a gold-standard manual tracing database. We segmented 460 subjects from ADNI, each subject having been scanned twice at baseline, 12-month and 24 month follow-up scan (1.5 T, T1 MRI). We used the bilateral hippocampal volume v and its variation, measured as the annualized volume change Λ =  δv / year ( mm 3 / y ). Four processing approaches with different complexity are compared to maximize the longitudinal information, and they are tested for cohort discrimination ability. Reference cohorts are Controls vs. Alzheimer's Disease (CTRL/AD) and CTRL vs. Mild Cognitive Impairment who subsequently progressed to AD dementia (CTRL/MCI -co ). We discuss the conditions on v and the added value of Λ in discriminating subjects. Results The age-corrected bilateral annualized atrophy rate (%/year) were: − 1.6 (0.6) for CTRL, − 2.2 (1.0) for MCI- nc , − 3.2 (1.2) for MCI- co and − 4.0 (1.5) for AD. Combined ( v , Λ) discrimination ability gave an Area under the ROC curve ( auc ) = 0.93 for CTRL vs AD and auc  = 0.88 for CTRL vs MCI- co . Conclusions Longitudinal volume measurements can provide meaningful clinical insight and added value with respect to the baseline provided the analysis procedure embeds the longitudinal information.